Karmaker, C. (2017). Determination of Optimum Smoothing Constant of Single Exponential Smoothing Method: A Case Study. International Journal of Research in Industrial Engineering, 6(3), 184-192. doi: 10.22105/riej.2017.49603
C. L. Karmaker. "Determination of Optimum Smoothing Constant of Single Exponential Smoothing Method: A Case Study". International Journal of Research in Industrial Engineering, 6, 3, 2017, 184-192. doi: 10.22105/riej.2017.49603
Karmaker, C. (2017). 'Determination of Optimum Smoothing Constant of Single Exponential Smoothing Method: A Case Study', International Journal of Research in Industrial Engineering, 6(3), pp. 184-192. doi: 10.22105/riej.2017.49603
Karmaker, C. Determination of Optimum Smoothing Constant of Single Exponential Smoothing Method: A Case Study. International Journal of Research in Industrial Engineering, 2017; 6(3): 184-192. doi: 10.22105/riej.2017.49603
Determination of Optimum Smoothing Constant of Single Exponential Smoothing Method: A Case Study
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh
Abstract
Exponential smoothing is a sophisticated forecasting method that works based on previous forecast plus a percentage of the forecast error. A key issue of this technique is the proper choice of exponential smoothing constant. In order to minimize forecasting errors, choosing an appropriate value of smoothing constant is very crucial. In this study, a framework is developed for the selection of optimal value of smoothing constant that minimizes a measure of forecast errors like mean square error (MSE) and mean absolute deviation (MAD). Both “trial & error” and Excel based non-linear optimizer (“Excel Solver”) are used for this purpose. To validate the proposed model, necessary demand data of Ruchi Jhal Muri from years 2010-2016 from Square Food & Beverage Ltd. in Mohakhali, Dhaka were collected. The optimum values of smoothing constant under trial & error method are 0.31 and 0.14 for minimum MAD and MSE respectively whereas for excel solver, values are 0.314 and 0.143 with respect to minimum MAD and MSE. Although both methods provide approximately the same results but excel solver is much easier & requires less time for deriving optimum solution. This study will provide an outline for the forecast planners as well as manufacturers to improve the accuracy of exponential forecasting through using Excel Solver for determining the optimum value of smoothing constant.
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